Back to Multiple platform build/check report for BioC 3.22: simplified long |
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This page was generated on 2025-09-27 12:05 -0400 (Sat, 27 Sep 2025).
Hostname | OS | Arch (*) | R version | Installed pkgs |
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nebbiolo2 | Linux (Ubuntu 24.04.3 LTS) | x86_64 | 4.5.1 Patched (2025-08-23 r88802) -- "Great Square Root" | 4832 |
lconway | macOS 12.7.1 Monterey | x86_64 | 4.5.1 Patched (2025-09-10 r88807) -- "Great Square Root" | 4620 |
kjohnson3 | macOS 13.7.7 Ventura | arm64 | 4.5.1 Patched (2025-09-10 r88807) -- "Great Square Root" | 4565 |
taishan | Linux (openEuler 24.03 LTS) | aarch64 | 4.5.0 (2025-04-11) -- "How About a Twenty-Six" | 4563 |
Click on any hostname to see more info about the system (e.g. compilers) (*) as reported by 'uname -p', except on Windows and Mac OS X |
Package 2109/2334 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
STATegRa 1.45.0 (landing page) David Gomez-Cabrero
| nebbiolo2 | Linux (Ubuntu 24.04.3 LTS) / x86_64 | OK | OK | OK | ![]() | ||||||||
lconway | macOS 12.7.1 Monterey / x86_64 | OK | OK | OK | OK | ![]() | ||||||||
kjohnson3 | macOS 13.7.7 Ventura / arm64 | OK | OK | OK | OK | ![]() | ||||||||
taishan | Linux (openEuler 24.03 LTS) / aarch64 | OK | OK | OK | ||||||||||
To the developers/maintainers of the STATegRa package: - Allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/STATegRa.git to reflect on this report. See Troubleshooting Build Report for more information. - Use the following Renviron settings to reproduce errors and warnings. - If 'R CMD check' started to fail recently on the Linux builder(s) over a missing dependency, add the missing dependency to 'Suggests:' in your DESCRIPTION file. See Renviron.bioc for more information. |
Package: STATegRa |
Version: 1.45.0 |
Command: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:STATegRa.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings STATegRa_1.45.0.tar.gz |
StartedAt: 2025-09-27 00:34:52 -0400 (Sat, 27 Sep 2025) |
EndedAt: 2025-09-27 00:38:41 -0400 (Sat, 27 Sep 2025) |
EllapsedTime: 228.5 seconds |
RetCode: 0 |
Status: OK |
CheckDir: STATegRa.Rcheck |
Warnings: 0 |
############################################################################## ############################################################################## ### ### Running command: ### ### /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:STATegRa.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings STATegRa_1.45.0.tar.gz ### ############################################################################## ############################################################################## * using log directory ‘/Users/biocbuild/bbs-3.22-bioc/meat/STATegRa.Rcheck’ * using R version 4.5.1 Patched (2025-09-10 r88807) * using platform: x86_64-apple-darwin20 * R was compiled by Apple clang version 14.0.0 (clang-1400.0.29.202) GNU Fortran (GCC) 14.2.0 * running under: macOS Monterey 12.7.6 * using session charset: UTF-8 * using option ‘--no-vignettes’ * checking for file ‘STATegRa/DESCRIPTION’ ... OK * checking extension type ... Package * this is package ‘STATegRa’ version ‘1.45.0’ * package encoding: UTF-8 * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking for sufficient/correct file permissions ... OK * checking whether package ‘STATegRa’ can be installed ... OK * checking installed package size ... OK * checking package directory ... OK * checking ‘build’ directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking code files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * checking whether the package can be loaded ... OK * checking whether the package can be loaded with stated dependencies ... OK * checking whether the package can be unloaded cleanly ... OK * checking whether the namespace can be loaded with stated dependencies ... OK * checking whether the namespace can be unloaded cleanly ... OK * checking dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... NOTE modelSelection,list-numeric-character: no visible binding for global variable ‘components’ modelSelection,list-numeric-character: no visible binding for global variable ‘mylabel’ plotVAF,caClass: no visible binding for global variable ‘comp’ plotVAF,caClass: no visible binding for global variable ‘VAF’ plotVAF,caClass: no visible binding for global variable ‘block’ selectCommonComps,list-numeric: no visible binding for global variable ‘comps’ selectCommonComps,list-numeric: no visible binding for global variable ‘block’ selectCommonComps,list-numeric: no visible binding for global variable ‘comp’ selectCommonComps,list-numeric: no visible binding for global variable ‘ratio’ Undefined global functions or variables: VAF block comp components comps mylabel ratio * checking Rd files ... OK * checking Rd metadata ... OK * checking Rd cross-references ... NOTE Found the following Rd file(s) with Rd \link{} targets missing package anchors: modelSelection.Rd: ggplot Please provide package anchors for all Rd \link{} targets not in the package itself and the base packages. * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking contents of ‘data’ directory ... OK * checking data for non-ASCII characters ... OK * checking data for ASCII and uncompressed saves ... OK * checking files in ‘vignettes’ ... OK * checking examples ... OK Examples with CPU (user + system) or elapsed time > 5s user system elapsed plotRes 5.578 0.306 5.923 plotVAF 4.805 0.211 5.050 * checking for unstated dependencies in ‘tests’ ... OK * checking tests ... Running ‘STATEgRa_Example.omicsCLUST.R’ Running ‘STATEgRa_Example.omicsPCA.R’ Running ‘STATegRa_Example.omicsNPC.R’ Running ‘runTests.R’ OK * checking for unstated dependencies in vignettes ... OK * checking package vignettes ... OK * checking running R code from vignettes ... SKIPPED * checking re-building of vignette outputs ... SKIPPED * checking PDF version of manual ... OK * DONE Status: 2 NOTEs See ‘/Users/biocbuild/bbs-3.22-bioc/meat/STATegRa.Rcheck/00check.log’ for details.
STATegRa.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL STATegRa ### ############################################################################## ############################################################################## * installing to library ‘/Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/library’ * installing *source* package ‘STATegRa’ ... ** this is package ‘STATegRa’ version ‘1.45.0’ ** using staged installation ** R ** data ** inst ** byte-compile and prepare package for lazy loading ** help *** installing help indices ** building package indices ** installing vignettes ** testing if installed package can be loaded from temporary location ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * DONE (STATegRa)
STATegRa.Rcheck/tests/runTests.Rout
R version 4.5.1 Patched (2025-09-10 r88807) -- "Great Square Root" Copyright (C) 2025 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > BiocGenerics:::testPackage("STATegRa") Common components [1] 2 Distinctive components [[1]] [1] 0 [[2]] [1] 0 Common components [1] 2 Distinctive components [[1]] [1] 1 [[2]] [1] 1 Common components [1] 2 Distinctive components [[1]] [1] 2 [[2]] [1] 2 RUNIT TEST PROTOCOL -- Sat Sep 27 00:38:31 2025 *********************************************** Number of test functions: 4 Number of errors: 0 Number of failures: 0 1 Test Suite : STATegRa RUnit Tests - 4 test functions, 0 errors, 0 failures Number of test functions: 4 Number of errors: 0 Number of failures: 0 Warning messages: 1: In rownames(pData) == colnames(exprs) : longer object length is not a multiple of shorter object length 2: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "%accum", : Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 2 3: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "fixed.num", : Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 3 > > proc.time() user system elapsed 3.537 0.287 3.832
STATegRa.Rcheck/tests/STATEgRa_Example.omicsCLUST.Rout
R version 4.5.1 Patched (2025-09-10 r88807) -- "Great Square Root" Copyright (C) 2025 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ########################################### > ########### EXAMPLE OF THE OMICSCLUSTERING > ########################################### > require(STATegRa) Loading required package: STATegRa > > ############################################# > ## PART 1: CREATING a bioMap CLASS > ############################################# > ####### This part creates or reads the map between features. > ####### In the present example the map is downloaded from a resource. > ####### then the class is created. > > #load("../data/STATegRa_S2.rda") > data(STATegRa_S2) > > MAP.SYMBOL<-bioMap(name = "Symbol-miRNA", + metadata = list(type_v1="Gene",type_v2="miRNA", + source_database="targetscan.Hs.eg.db", + data_extraction="July2014"), + map=mapdata) > > > ############################################# > ## PART 2: CREATING a bioDist CLASS > ############################################# > ##### In the second part given a set of main features and surrogate feautres, > ##### the profile of the main features is computed through the surrogate features. > > # Load Data > data(STATegRa_S1) > #load("../data/STATegRa.S1.Rdata") > > ## Create ExpressionSets > # source("../R/STATegRa_omicsPCA_classes_and_methods.R") > # Block1 - Expression data > mRNA.ds <- createOmicsExpressionSet(Data=Block1,pData=ed,pDataDescr=c("classname")) > # Block2 - miRNA expression data > miRNA.ds <- createOmicsExpressionSet(Data=Block2,pData=ed,pDataDescr=c("classname")) > > # Create Gene-gene distance computed through miRNA data > bioDistmiRNA<-bioDist(referenceFeatures = rownames(Block1), + reference = "Var1", + mapping = MAP.SYMBOL, + surrogateData = miRNA.ds, ### miRNA data + referenceData = mRNA.ds, ### mRNA data + maxitems=2, + selectionRule="sd", + expfac=NULL, + aggregation = "sum", + distance = "spearman", + noMappingDist = 0, + filtering = NULL, + name = "mRNAbymiRNA") > > require(Biobase) Loading required package: Biobase Loading required package: BiocGenerics Loading required package: generics Attaching package: 'generics' The following objects are masked from 'package:base': as.difftime, as.factor, as.ordered, intersect, is.element, setdiff, setequal, union Attaching package: 'BiocGenerics' The following objects are masked from 'package:stats': IQR, mad, sd, var, xtabs The following objects are masked from 'package:base': Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind, rownames, sapply, saveRDS, table, tapply, unique, unsplit, which.max, which.min Welcome to Bioconductor Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see 'citation("Biobase")', and for packages 'citation("pkgname")'. > > # Create Gene-gene distance through mRNA data > bioDistmRNA<-bioDistclass(name = "mRNAbymRNA", + distance = cor(t(exprs(mRNA.ds)),method="spearman"), + map.name = "id", + map.metadata = list(), + params = list()) > > ############################################# > ## PART 3: CREATING a LISTOF WEIGTHED DISTANCES MATRICES: bioDistWList > ############################################# > > bioDistList<-list(bioDistmRNA,bioDistmiRNA) > weights<-matrix(0,4,2) > weights[,1]<-c(0,0.33,0.67,1) > weights[,2]<-c(1,0.67,0.33,0)# > > bioDistWList<-bioDistW(referenceFeatures = rownames(Block1), + bioDistList = bioDistList, + weights=weights) > length(bioDistWList) [1] 4 > > ############################################# > ## PART 4: DEFINING THE STRENGTH OF ASSOCIATIONS IN GENERAL > ############################################# > > bioDistWPlot(referenceFeatures = rownames(Block1) , + listDistW = bioDistWList, + method.cor="spearman") Warning messages: 1: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], : Cannot compute exact p-value with ties 2: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], : Cannot compute exact p-value with ties 3: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], : Cannot compute exact p-value with ties > > ############################################# > ## PART 5: DEFINING THE ASSOCIATIONS FOR A GIVEN GENE > ############################################# > > ## IDH1 > > IDH1.F<-bioDistFeature(Feature = "IDH1" , + listDistW = bioDistWList, + threshold.cor=0.7) > bioDistFeaturePlot(data=IDH1.F) > > ## PDGFRA > > #PDGFRA.F<-bioDistFeature(Feature = "PDGFRA" , > # listDistW = bioDistWList, > # threshold.cor=0.7) > #bioDistFeaturePlot(data=PDGFRA.F,name="../vignettes/PDGFRA.png") > > ## EGFR > #EGFR.F<-bioDistFeature(Feature = "EGFR" , > # listDistW = bioDistWList, > # threshold.cor=0.7) > #bioDistFeaturePlot(data=EGFR.F,name="../vignettes/EGFR.png") > > ## MGMT > #MGMT.F<-bioDistFeature(Feature = "MGMT" , > # listDistW = bioDistWList, > # threshold.cor=0.5) > #bioDistFeaturePlot(data=MGMT.F,name="../vignettes/MGMT.png") > > > > > > proc.time() user system elapsed 27.697 0.794 28.687
STATegRa.Rcheck/tests/STATegRa_Example.omicsNPC.Rout
R version 4.5.1 Patched (2025-09-10 r88807) -- "Great Square Root" Copyright (C) 2025 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > rm(list = ls()) > require("STATegRa") Loading required package: STATegRa > # Load the data > data("TCGA_BRCA_Batch_93") > # Setting dataTypes > dataTypes <- c("count", "count", "continuous") > # Setting methods to combine pvalues > combMethods = c("Fisher", "Liptak", "Tippett") > # Setting number of permutations > numPerms = 1000 > # Setting number of cores > numCores = 1 > # Setting holistOmics to print out the steps that it performs. > verbose = TRUE > # Run holistOmics analysis. > output <- omicsNPC(dataInput = TCGA_BRCA_Data, dataTypes = dataTypes, combMethods = combMethods, numPerms = numPerms, numCores = numCores, verbose = verbose) Compute initial statistics on data Building NULL distributions by permuting data Compute pseudo p-values based on NULL distributions... NPC p-values calculation... > > proc.time() user system elapsed 71.206 1.444 73.076
STATegRa.Rcheck/tests/STATEgRa_Example.omicsPCA.Rout
R version 4.5.1 Patched (2025-09-10 r88807) -- "Great Square Root" Copyright (C) 2025 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ########################################### > ########### EXAMPLE OF THE OMICSPCA > ########################################### > require(STATegRa) Loading required package: STATegRa > > # g_legend (not exported by STATegRa any more) > ## code from https://github.com/hadley/ggplot2/wiki/Share-a-legend-between-two-ggplot2-graphs > g_legend<-function(a.gplot){ + tmp <- ggplot_gtable(ggplot_build(a.gplot)) + leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box") + legend <- tmp$grobs[[leg]] + return(legend)} > > ######################### > ## PART 1. Load data > > ## Load data > data(STATegRa_S3) > > ls() [1] "Block1.PCA" "Block2.PCA" "ed.PCA" "g_legend" > > ## Create ExpressionSets > # Block1 - Expression data > B1 <- createOmicsExpressionSet(Data=Block1.PCA,pData=ed.PCA,pDataDescr=c("classname")) > # Block2 - miRNA expression data > B2 <- createOmicsExpressionSet(Data=Block2.PCA,pData=ed.PCA,pDataDescr=c("classname")) > > ######################### > ## PART 2. Model Selection > > require(grid) Loading required package: grid > require(gridExtra) Loading required package: gridExtra > require(ggplot2) Loading required package: ggplot2 > > ## Select the optimal components > ms <- modelSelection(Input=list(B1,B2),Rmax=4,fac.sel="single%",varthreshold=0.03,center=TRUE,scale=TRUE,weight=TRUE) Common components [1] 2 Distinctive components [[1]] [1] 2 [[2]] [1] 2 > > > ######################### > ## PART 3. Component Analysis > > ## 3.1 Component analysis of the three methods > discoRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="DISCOSCA",Rcommon=2,Rspecific=c(2,2),center=TRUE, + scale=TRUE,weight=TRUE) > jiveRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="JIVE",Rcommon=2,Rspecific=c(2,2),center=TRUE, + scale=TRUE,weight=TRUE) > o2plsRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="O2PLS",Rcommon=2,Rspecific=c(2,2),center=TRUE, + scale=TRUE,weight=TRUE) > > ## 3.2 Exploring scores structures > > # Exploring DISCO-SCA scores structure > discoRes@scores$common ## Common scores 1 2 sample1 -0.0781575647 0.0431550034 sample2 0.1192221450 -0.0294019621 sample3 0.0531408604 0.0746837511 sample4 -0.0292971696 0.0006036398 sample5 -0.0202090714 -0.0110455015 sample6 -0.1226088428 -0.1053494037 sample7 -0.1078931423 0.0322417227 sample8 -0.1782891034 -0.1449329304 sample9 -0.0468697271 0.0455171617 sample10 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0.0979805053 0.0121500025 sample94 -0.0131486270 0.0114929329 sample95 -0.0315682455 0.0758919203 sample96 -0.0024125838 0.0470186931 sample97 -0.0634545804 -0.0270302743 sample98 0.0359372502 0.0135465687 sample99 0.1009167734 -0.1124710254 sample100 -0.0551754122 -0.0246502569 sample101 0.0080115912 0.1627408585 sample102 0.0046451378 -0.0095467354 sample103 0.0472520802 0.0940383175 sample104 -0.0198157403 0.0591149157 sample105 0.0400239031 0.0160950412 sample106 0.0923810184 -0.0369003357 sample107 0.1019372332 -0.0224967793 sample108 0.0877091511 0.0128850264 sample109 -0.0864820169 0.0901085833 sample110 0.1223116501 0.0096109322 sample111 -0.0257352368 0.0936285020 sample112 0.0765285902 -0.0270380457 sample113 -0.0258799750 -0.0377436390 sample114 -0.0021141147 0.0882041000 sample115 -0.0303455161 0.0723740397 sample116 -0.0780504368 0.0685165708 sample117 -0.0536893960 0.0912029455 sample118 -0.0666649867 0.0236262141 sample119 -0.1021872617 0.2325006086 sample120 -0.0750216319 -0.0243344519 sample121 0.0756937921 -0.0942971132 sample122 0.0259632181 -0.0731918950 sample123 0.1037844642 0.0369178122 sample124 -0.0611205089 -0.0421644463 sample125 0.0738472625 -0.0066944069 sample126 -0.0972919200 -0.0762700927 sample127 -0.0824699483 0.0096644899 sample128 0.1249411631 -0.0929251812 sample129 0.0734063592 0.0434311920 sample130 0.0003500210 0.0309857355 sample131 -0.0930184084 -0.0155971224 sample132 -0.0736220534 -0.0732970211 sample133 0.0498398371 0.0462456575 sample134 -0.1644872611 -0.0720048343 sample135 0.0752294983 -0.0003871378 sample136 -0.0227150082 0.0495468148 sample137 -0.0564721822 0.0288858718 sample138 -0.0255986440 0.0610933067 sample139 -0.0621218810 -0.0235859437 sample140 0.0604148846 0.0435530255 sample141 -0.0246743007 -0.0532629730 sample142 0.0409563966 -0.0316232709 sample143 0.0077356418 0.0476909295 sample144 -0.0173241004 0.0156786113 sample145 -0.0485467529 -0.1202736785 sample146 -0.0419650102 0.0811239542 sample147 0.0977304546 0.0274768986 sample148 -0.0368253200 -0.0803969166 sample149 0.0072864841 0.1533018097 sample150 -0.1020825522 -0.0624824162 sample151 -0.0305397106 0.0289339016 sample152 0.0533595211 0.0638335916 sample153 0.0891639741 -0.1799448907 sample154 0.0727554309 0.0834128366 sample155 0.0880665749 0.0220768689 sample156 0.0276558794 0.0326600973 sample157 0.1155031543 -0.0183636170 sample158 0.0281506679 0.0104911030 sample159 -0.0663233721 -0.0443808778 sample160 0.0302644017 -0.0404302565 sample161 -0.0114712890 0.0591085105 sample162 0.1337091101 -0.1398131268 sample163 -0.1330120647 -0.1688768930 sample164 0.0150338208 -0.0028374026 sample165 -0.0076518794 0.0164146411 sample166 -0.0367791413 -0.0630612606 sample167 -0.1111989842 -0.0030066749 sample168 0.0672983017 -0.0446266285 sample169 0.0413003598 -0.0224448848 > discoRes@scores$dist[[1]] ## Distinctive scores for Block 1 1 2 sample1 0.0420461958 0.0867866159 sample2 0.0820849951 -0.0410968684 sample3 -0.0155967004 -0.0195186399 sample4 0.1001342835 -0.0410776221 sample5 0.0153479882 -0.0253257707 sample6 -0.0340238055 -0.0408223388 sample7 -0.0722602675 0.0002323800 sample8 0.0457620928 -0.0370006814 sample9 0.0086216299 0.0820184488 sample10 0.0423631597 -0.0083917568 sample11 -0.0022593995 0.0787764090 sample12 -0.0322075771 0.1479823366 sample13 0.0293971178 -0.0306742813 sample14 -0.0337430273 -0.0367508390 sample15 -0.0815560438 0.1275613717 sample16 -0.0508330540 0.0540604320 sample17 -0.0062554725 0.0041024883 sample18 -0.0705600399 -0.0351053443 sample19 0.0476782998 -0.0509595391 sample20 -0.0523027926 0.0715513962 sample21 0.0119252526 -0.0376087021 sample22 -0.0724458784 -0.0095635048 sample23 0.0992529515 0.0134299116 sample24 0.1595267318 0.0728684529 sample25 0.0920660681 -0.0749749133 sample26 0.0595567353 0.0848973779 sample27 -0.0826578158 -0.0086747729 sample28 0.0384834183 0.0440972815 sample29 -0.0777741718 0.1735298356 sample30 -0.1229474387 -0.0819018681 sample31 -0.0579749376 -0.0238646909 sample32 -0.0970365415 -0.0111435286 sample33 -0.1017580023 -0.0630452752 sample34 -0.0637902410 0.0377936119 sample35 -0.0790003627 -0.0229732632 sample36 -0.1224933029 -0.1274968570 sample37 -0.1798847818 -0.1673448370 sample38 -0.0466394940 0.0888153061 sample39 0.0168694807 0.0421536080 sample40 -0.1756418298 -0.1526662688 sample41 -0.0042470519 0.0004924511 sample42 0.0447825220 -0.0651501320 sample43 -0.0482291813 -0.0253533612 sample44 0.1986820674 -0.0545753381 sample45 0.0741919061 0.0054714383 sample46 -0.0478863177 -0.0007080509 sample47 -0.0608216937 0.0481615324 sample48 0.1381464457 0.0578301243 sample49 0.0530632209 -0.1405523452 sample50 0.0173644755 0.1602386081 sample51 -0.0462455394 0.0303473001 sample52 -0.0279995211 0.0280387844 sample53 -0.0667497246 0.0237700090 sample54 -0.0121811441 -0.0521354918 sample55 -0.0182392165 0.0221326599 sample56 0.0001309525 0.0030909318 sample57 -0.0316573587 0.0530190638 sample58 -0.0393890700 -0.0297801847 sample59 -0.1278271173 -0.0546540818 sample60 -0.1486964378 0.1069141594 sample61 -0.0793066882 0.0569790292 sample62 -0.1172822125 -0.0149211392 sample63 0.0028813675 0.1300524193 sample64 -0.0237295451 0.1073288390 sample65 0.0126543752 0.0589810388 sample66 0.0468234320 -0.0771066509 sample67 -0.1494286234 -0.0769877687 sample68 -0.0978024216 -0.0577364042 sample69 -0.0403090513 0.0156038182 sample70 -0.0221598735 0.0315436553 sample71 0.0546328856 -0.0272394925 sample72 -0.1107501185 -0.0537331592 sample73 -0.0906756616 0.0579957725 sample74 -0.0586512923 0.0121417375 sample75 -0.0390512862 0.0349278130 sample76 0.0022939301 -0.1676560116 sample77 0.0232101503 -0.2067300910 sample78 0.0929810419 -0.0434927749 sample79 0.1619379517 -0.0378102267 sample80 -0.0680393065 0.1424655785 sample81 0.0530724890 -0.0358347614 sample82 -0.0266850776 -0.0577449162 sample83 -0.1517242123 -0.0448570384 sample84 0.0570942945 -0.0273808376 sample85 -0.1086271337 -0.1228130626 sample86 -0.0833892009 -0.0442924982 sample87 -0.0022041034 -0.0943908532 sample88 0.0078277391 -0.1140504509 sample89 -0.0611005311 -0.0094589473 sample90 -0.0022941780 -0.0936254884 sample91 -0.0433775235 0.3205971968 sample92 0.1815216398 -0.0334666437 sample93 -0.0267654320 0.0614425746 sample94 -0.0181901737 0.0605088126 sample95 0.0720313619 -0.0013040477 sample96 0.0559672206 -0.0118787064 sample97 0.0217420754 0.0195417254 sample98 -0.0379199517 0.0588352681 sample99 0.0792509154 -0.0151262182 sample100 -0.0222100153 -0.0023322961 sample101 0.0387082573 0.1224225192 sample102 0.2094626153 -0.0516420490 sample103 -0.0138558861 0.0301047563 sample104 0.0807947566 -0.0162712278 sample105 0.0520491841 -0.1229660273 sample106 0.0192643316 -0.0185235115 sample107 -0.0319014328 0.0405120527 sample108 0.0140673981 0.0163422356 sample109 0.1831856080 0.0613023961 sample110 0.0292782526 -0.0199846436 sample111 0.1423172855 0.0327352288 sample112 -0.0426313032 -0.0029087119 sample113 0.0771932481 0.0268742891 sample114 0.0241567037 -0.0184080653 sample115 0.1958955361 0.0460148901 sample116 0.1394436884 -0.0530793281 sample117 0.1672311088 -0.1386521707 sample118 0.0448331419 -0.0117617917 sample119 0.0910190244 0.2217435813 sample120 0.0331405128 -0.0057270261 sample121 -0.0307515601 0.1392506190 sample122 0.0839838730 -0.0291983433 sample123 -0.0239675755 -0.0642167463 sample124 0.0909176832 0.0130430308 sample125 0.0065362587 -0.1092631006 sample126 -0.0935272560 0.1368276698 sample127 -0.0035406024 0.0292754998 sample128 0.0660351287 0.1018575952 sample129 -0.0693671768 -0.0695430405 sample130 -0.0008517586 -0.0669705406 sample131 -0.0431011710 0.0174060936 sample132 0.0637090019 0.0029383658 sample133 0.0289463999 -0.0390817273 sample134 -0.0446140782 0.0456332258 sample135 -0.0712343915 0.0521627469 sample136 -0.0596319468 0.0197291555 sample137 -0.0793176017 -0.0380637453 sample138 0.0973504680 -0.0454209977 sample139 -0.0539865287 -0.1534332195 sample140 -0.0850873074 0.0955804254 sample141 0.0192724785 -0.0554446411 sample142 0.0672295108 -0.0461312939 sample143 0.0303706615 -0.0519258511 sample144 0.0089350269 0.0145815376 sample145 0.0638879246 0.0122268908 sample146 -0.0585924064 0.0063074747 sample147 -0.0894147117 -0.1124625975 sample148 0.0216441241 -0.0615962329 sample149 0.0515314134 -0.0839902839 sample150 -0.0568227568 -0.0124472812 sample151 0.0789513249 -0.0261823814 sample152 0.0330692003 0.1306445029 sample153 0.1752068301 0.1497755922 sample154 -0.0421491233 -0.0037017237 sample155 -0.0680199285 0.0095703367 sample156 -0.0388951081 0.1057557858 sample157 -0.0314764992 0.0561364596 sample158 -0.0329630466 0.0353943549 sample159 0.0398462628 -0.1007368189 sample160 -0.0424905026 0.0108492988 sample161 0.0888339536 -0.0679692686 sample162 0.0027573007 0.1237848364 sample163 0.0126232253 0.0725440996 sample164 0.0566787374 -0.0458318186 sample165 0.0315331452 -0.0236359532 sample166 0.0612110429 -0.0425224681 sample167 -0.0142729546 0.0179306967 sample168 0.0169543905 -0.0769614816 sample169 -0.0675063003 0.0131498954 > discoRes@scores$dist[[2]] ## Distinctive scores for Block 2 1 2 sample1 -0.0012331729 1.635716e-01 sample2 -0.0724353319 6.022150e-03 sample3 -0.0188459928 1.080029e-01 sample4 0.0390143059 -3.106322e-04 sample5 0.1774810594 2.996430e-02 sample6 -0.0451446530 3.455899e-02 sample7 -0.0226463391 7.019186e-03 sample8 -0.1033684700 9.857977e-03 sample9 0.1350014307 -8.979114e-02 sample10 0.1259884323 5.097940e-02 sample11 0.0979791013 -7.086567e-02 sample12 -0.0863021047 8.620322e-02 sample13 -0.1381401856 -1.827998e-01 sample14 -0.0615074768 2.642808e-02 sample15 0.0381600642 3.101600e-02 sample16 -0.0048779484 -1.271003e-03 sample17 -0.0788483307 1.547607e-02 sample18 -0.0884189528 3.795477e-02 sample19 0.0703043487 1.084003e-01 sample20 -0.0025581211 -7.975972e-02 sample21 0.0941596463 4.126897e-02 sample22 -0.0550270777 7.806614e-02 sample23 0.0679492738 4.102079e-02 sample24 -0.1310969665 -1.649282e-01 sample25 0.0113583507 4.426901e-02 sample26 -0.1402949011 -2.016457e-02 sample27 0.0261566212 -1.589998e-03 sample28 -0.0724200855 -5.850508e-02 sample29 -0.0330054636 -2.062110e-03 sample30 -0.0228750264 2.015342e-02 sample31 -0.0635070495 6.670370e-02 sample32 0.0685100011 4.955245e-02 sample33 -0.0777764922 1.272070e-01 sample34 0.0157842070 3.024311e-02 sample35 -0.0529627768 -1.500981e-01 sample36 0.0070908227 -2.025321e-01 sample37 -0.0442411507 -1.802109e-01 sample38 -0.0781508276 3.676295e-02 sample39 0.0120330006 3.388885e-02 sample40 -0.0473283561 -1.471582e-01 sample41 0.0228192287 2.673456e-02 sample42 -0.0245361916 7.960878e-02 sample43 0.1036361982 8.229578e-02 sample44 -0.1012234977 -7.049235e-02 sample45 0.0013726510 2.451073e-02 sample46 -0.0558506332 -2.948613e-03 sample47 -0.0380478633 -4.554238e-02 sample48 0.0784340414 -4.888889e-02 sample49 -0.0605168213 1.162474e-02 sample50 0.0530083046 2.737811e-02 sample51 0.1514645311 -5.678258e-02 sample52 0.1860936039 -1.246711e-01 sample53 -0.0064179798 2.701061e-02 sample54 0.0697037533 2.308413e-02 sample55 0.1633577729 -1.366432e-02 sample56 0.1011483958 -4.682132e-02 sample57 0.1730374413 -1.609594e-01 sample58 -0.0071384898 1.666951e-02 sample59 -0.0030458369 -3.005378e-02 sample60 0.0215842433 -2.665887e-01 sample61 0.1510585391 -1.002384e-01 sample62 -0.0925531509 4.845725e-02 sample63 -0.0596315561 4.137110e-02 sample64 -0.0449227331 2.600968e-03 sample65 0.0939382197 4.406951e-02 sample66 0.1063397632 5.710080e-02 sample67 -0.0201580499 -2.361746e-01 sample68 0.0037208565 -2.418546e-02 sample69 -0.0645162025 1.155618e-01 sample70 -0.1013439738 1.351780e-01 sample71 -0.0016466041 2.976772e-02 sample72 0.0328895482 2.835769e-02 sample73 0.0275080396 5.148152e-02 sample74 0.1341718320 7.895303e-02 sample75 0.0951576678 3.943147e-02 sample76 -0.0864719878 -3.035055e-02 sample77 -0.1035749503 2.545324e-02 sample78 -0.1575647998 -4.939472e-02 sample79 0.0189138428 -4.874690e-02 sample80 0.1384142846 -4.316120e-05 sample81 -0.0118846670 6.357908e-02 sample82 -0.1675306590 -3.533970e-02 sample83 -0.0065671085 7.812495e-02 sample84 0.1486890628 3.109097e-02 sample85 -0.0532720209 -7.417991e-02 sample86 -0.1138474839 1.817858e-05 sample87 0.0432866015 -6.080500e-02 sample88 0.0433451223 -1.402486e-01 sample89 0.0331204764 1.395429e-02 sample90 -0.0607413501 8.610385e-02 sample91 -0.0566263438 -1.303770e-01 sample92 -0.0359580626 -1.061605e-01 sample93 -0.0433646461 4.443609e-02 sample94 -0.0477292157 1.059571e-01 sample95 -0.0249595925 3.980509e-02 sample96 0.0035217534 9.293931e-02 sample97 -0.0066052112 1.527234e-01 sample98 0.0020367072 5.579514e-02 sample99 -0.0886621919 3.728377e-02 sample100 -0.1091259619 3.560401e-02 sample101 -0.0739723739 4.317883e-02 sample102 0.0574455534 2.784090e-02 sample103 0.0142733842 -9.706370e-03 sample104 0.0710395599 -4.068330e-02 sample105 0.0980829894 3.452998e-02 sample106 -0.0254260534 -3.628932e-02 sample107 -0.0160655096 9.173398e-02 sample108 -0.0200988334 2.379699e-02 sample109 -0.0389781973 -1.692310e-02 sample110 -0.0326305265 -2.988086e-02 sample111 0.0676935890 6.038250e-02 sample112 0.0167883511 -5.336924e-03 sample113 0.0969213856 2.757705e-02 sample114 -0.0026397962 9.209101e-02 sample115 -0.0308049630 -1.603742e-02 sample116 -0.1240306360 -1.272998e-01 sample117 0.0334728644 -5.392661e-02 sample118 -0.1037152129 -6.252440e-02 sample119 -0.1064170371 -1.196218e-01 sample120 -0.0771357794 1.004935e-01 sample121 -0.0129352368 -3.181913e-02 sample122 0.0847487403 5.568467e-02 sample123 -0.0041335479 -7.693560e-03 sample124 -0.0583462339 8.396478e-02 sample125 0.0634843205 5.232568e-02 sample126 -0.0662582138 1.091730e-01 sample127 -0.0865025648 1.094172e-01 sample128 -0.0627822181 1.471096e-02 sample129 -0.0336274532 4.007773e-02 sample130 -0.0293518127 8.046086e-02 sample131 -0.0469196764 2.209376e-03 sample132 -0.0241745795 1.248608e-01 sample133 0.0907303819 -1.466698e-02 sample134 -0.0350841202 -7.539660e-02 sample135 0.0001334927 -9.185828e-03 sample136 -0.0335874766 9.860180e-02 sample137 -0.0640147220 7.554369e-02 sample138 0.0060964026 1.742783e-02 sample139 -0.0592082709 -5.615007e-02 sample140 0.0427988690 1.099464e-02 sample141 0.0618793170 9.301103e-02 sample142 0.0898552446 -3.573322e-02 sample143 0.0817391127 -8.880528e-02 sample144 0.0787754466 3.821395e-02 sample145 0.1085819446 -1.569460e-01 sample146 -0.0589554907 4.373234e-02 sample147 -0.0495327835 -7.278078e-03 sample148 0.1161590411 -9.078115e-03 sample149 -0.0121575372 -7.788464e-02 sample150 -0.0314511961 -3.520220e-02 sample151 0.0575380909 1.945393e-02 sample152 -0.0494540298 -7.025566e-02 sample153 -0.0941338719 -2.153269e-01 sample154 -0.0335928728 -2.078827e-02 sample155 0.0690459086 2.780360e-02 sample156 0.1039902325 6.292488e-02 sample157 -0.0408645846 -8.065531e-03 sample158 0.1018106375 -7.817023e-03 sample159 -0.0281732592 1.207261e-02 sample160 0.1643052857 -2.977803e-03 sample161 0.0374330114 -8.524588e-02 sample162 -0.0804538363 -8.349633e-02 sample163 -0.0743232536 1.406349e-02 sample164 0.1208804226 2.139525e-02 sample165 0.1608115957 -2.025158e-02 sample166 -0.0425948037 2.660802e-02 sample167 -0.0226849506 4.464257e-02 sample168 -0.0180737427 7.471652e-04 sample169 0.0190780243 -2.645427e-02 > # Exploring O2PLS scores structure > o2plsRes@scores$common[[1]] ## Common scores for Block 1 [,1] [,2] sample1 -0.0572060227 -1.729087e-02 sample2 0.0875245208 1.112588e-02 sample3 0.0403482602 -3.168994e-02 sample4 -0.0218345996 4.052760e-06 sample5 -0.0150905011 4.795041e-03 sample6 -0.0924362933 4.511003e-02 sample7 -0.0793066751 -1.243823e-02 sample8 -0.1342997187 6.215220e-02 sample9 -0.0338886944 -1.854401e-02 sample10 0.0020547173 1.749421e-02 sample11 0.0037275602 -2.364116e-02 sample12 -0.0753094533 2.772698e-02 sample13 0.0856160091 3.679963e-02 sample14 -0.0737457307 2.668452e-02 sample15 -0.0062111746 -3.554864e-03 sample16 -0.0602355268 6.675115e-02 sample17 0.1086768843 2.524534e-02 sample18 0.0702999472 2.231671e-02 sample19 0.0173785882 -3.024846e-02 sample20 0.0484173812 -3.310904e-02 sample21 0.0124657042 6.517144e-02 sample22 -0.0140989936 -3.159137e-02 sample23 -0.0627028403 -5.393710e-04 sample24 0.0919972100 7.909297e-02 sample25 0.0326998483 -1.945206e-02 sample26 0.1064741246 2.120849e-02 sample27 0.0166058995 -4.964993e-02 sample28 0.0743504770 2.614211e-02 sample29 -0.0511008491 -2.782647e-02 sample30 0.0962250842 -3.974893e-03 sample31 -0.0869563008 5.250819e-02 sample32 0.0271858919 1.552005e-02 sample33 -0.0448364581 6.243160e-03 sample34 0.0718415218 1.469396e-02 sample35 0.0403086451 -1.632629e-02 sample36 -0.1036402827 -1.304320e-02 sample37 -0.0159385744 -3.036525e-02 sample38 0.0182198369 -4.034805e-02 sample39 0.0690363619 8.058350e-03 sample40 -0.0467312750 -2.810325e-02 sample41 0.0263674438 -5.171216e-02 sample42 0.0374578960 -1.268634e-02 sample43 0.0132336869 9.536642e-03 sample44 -0.1119154428 5.028683e-02 sample45 0.0759639367 4.587903e-02 sample46 0.0871885519 -4.670385e-02 sample47 0.0721490571 -1.288540e-02 sample48 0.0005086144 -1.290565e-02 sample49 -0.0858177028 5.173760e-02 sample50 0.0118992665 -7.276215e-02 sample51 -0.0426446855 5.306205e-02 sample52 -0.0381605826 3.086785e-02 sample53 -0.0855757630 6.730043e-02 sample54 0.0261723092 9.184260e-03 sample55 -0.0156418304 4.682404e-04 sample56 0.0307831193 2.597550e-02 sample57 -0.0157242103 4.829381e-02 sample58 -0.0031174404 1.359898e-02 sample59 -0.0373001859 5.868397e-03 sample60 -0.0142609099 5.831654e-03 sample61 -0.0122255144 2.663579e-02 sample62 0.0228002942 -8.692265e-03 sample63 -0.0833127581 5.473229e-02 sample64 -0.1166548159 4.196500e-02 sample65 0.0038808902 8.568590e-03 sample66 0.0011561811 1.766612e-02 sample67 -0.1129311062 -2.608702e-02 sample68 -0.0382526429 -3.804045e-02 sample69 -0.0476502440 4.003241e-03 sample70 -0.0110329882 -2.752719e-02 sample71 0.0096850282 -5.627056e-02 sample72 0.0487124704 -8.800131e-03 sample73 0.0773058132 8.239864e-03 sample74 -0.0102488176 2.454957e-02 sample75 -0.0286613976 -8.387293e-03 sample76 -0.0472655595 -2.129315e-02 sample77 -0.0865043074 -7.296820e-03 sample78 0.1070293698 2.818346e-02 sample79 -0.0165060681 -6.659721e-02 sample80 -0.0206765949 -8.712112e-03 sample81 -0.0050943615 -3.079175e-02 sample82 0.1153622361 -1.647054e-02 sample83 0.0367979217 -2.538114e-03 sample84 0.0199463070 -1.468961e-02 sample85 -0.0827122185 -2.709824e-04 sample86 0.0969487314 -1.699897e-02 sample87 0.0421957457 -1.965953e-02 sample88 0.0215934743 1.566050e-02 sample89 0.0751559502 2.811652e-02 sample90 -0.0057328000 -8.283795e-03 sample91 -0.1134005268 -8.603522e-02 sample92 -0.0101689918 -6.894992e-02 sample93 0.0725967502 -6.003176e-03 sample94 -0.0096878852 -4.693081e-03 sample95 -0.0223502239 -3.139636e-02 sample96 -0.0013232863 -1.963604e-02 sample97 -0.0476541710 1.183660e-02 sample98 0.0269546160 -5.978398e-03 sample99 0.0728179461 4.597884e-02 sample100 -0.0413398038 1.079347e-02 sample101 0.0087536994 -6.796076e-02 sample102 0.0032509529 3.932612e-03 sample103 0.0360342395 -3.973263e-02 sample104 -0.0141722563 -2.453107e-02 sample105 0.0294940465 -7.140722e-03 sample106 0.0686472054 1.462895e-02 sample107 0.0748635927 8.401339e-03 sample108 0.0650175850 -6.211942e-03 sample109 -0.0628017242 -3.681224e-02 sample110 0.0905513691 -5.169053e-03 sample111 -0.0176679473 -3.884777e-02 sample112 0.0570870472 1.066018e-02 sample113 -0.0200110554 1.596044e-02 sample114 -0.0001474542 -3.679272e-02 sample115 -0.0213333038 -2.991667e-02 sample116 -0.0567675453 -2.785636e-02 sample117 -0.0379865990 -3.752078e-02 sample118 -0.0484878786 -9.173691e-03 sample119 -0.0713511831 -9.598634e-02 sample120 -0.0555093586 1.089843e-02 sample121 0.0542443861 3.861344e-02 sample122 0.0178575357 3.027138e-02 sample123 0.0775020581 -1.636852e-02 sample124 -0.0460701050 1.814758e-02 sample125 0.0543846585 2.075898e-03 sample126 -0.0729417144 3.276659e-02 sample127 -0.0609509157 -3.270814e-03 sample128 0.0908136899 3.758801e-02 sample129 0.0552445878 -1.879062e-02 sample130 0.0007128089 -1.294308e-02 sample131 -0.0693311345 7.357082e-03 sample132 -0.0556565156 3.126995e-02 sample133 0.0375870104 -1.977240e-02 sample134 -0.1229130924 3.159495e-02 sample135 0.0555550315 -5.563250e-04 sample136 -0.0159768414 -2.046339e-02 sample137 -0.0412337694 -1.151652e-02 sample138 -0.0180604476 -2.526505e-02 sample139 -0.0465649201 1.040683e-02 sample140 0.0452288969 -1.876279e-02 sample141 -0.0189142561 2.247042e-02 sample142 0.0297545566 1.280524e-02 sample143 0.0064292003 -1.997706e-02 sample144 -0.0124284903 -6.369733e-03 sample145 -0.0377141491 5.066743e-02 sample146 -0.0296240067 -3.344465e-02 sample147 0.0726083535 -1.239968e-02 sample148 -0.0284795794 3.389732e-02 sample149 0.0082261455 -6.399305e-02 sample150 -0.0765013197 2.704021e-02 sample151 -0.0220567356 -1.178159e-02 sample152 0.0403422737 -2.714879e-02 sample153 0.0629117719 7.425085e-02 sample154 0.0551622927 -3.548984e-02 sample155 0.0654439133 -1.005306e-02 sample156 0.0209310714 -1.390213e-02 sample157 0.0851522597 6.577150e-03 sample158 0.0208354599 -4.663078e-03 sample159 -0.0498794349 1.913257e-02 sample160 0.0216074437 1.656579e-02 sample161 -0.0075742328 -2.455676e-02 sample162 0.0963663017 5.705881e-02 sample163 -0.1009542191 7.174224e-02 sample164 0.0109881996 1.026806e-03 sample165 -0.0053146157 -6.772855e-03 sample166 -0.0275757357 2.673084e-02 sample167 -0.0825048036 2.278863e-03 sample168 0.0486147429 1.793843e-02 sample169 0.0302506727 8.984253e-03 > o2plsRes@scores$common[[2]] ## Common scores for Block 2 [,1] [,2] sample1 -0.0621842115 -1.364509e-02 sample2 0.0944623785 9.720892e-03 sample3 0.0406196267 -2.236338e-02 sample4 -0.0229316496 -3.932487e-04 sample5 -0.0157330047 3.231033e-03 sample6 -0.0945794025 3.120720e-02 sample7 -0.0854427118 -1.052880e-02 sample8 -0.1376625920 4.286608e-02 sample9 -0.0377115311 -1.415134e-02 sample10 0.0035244506 1.280825e-02 sample11 0.0016639987 -1.717895e-02 sample12 -0.0781403168 1.884368e-02 sample13 0.0938400516 2.838858e-02 sample14 -0.0759839772 1.810989e-02 sample15 -0.0068340837 -2.705361e-03 sample16 -0.0590150849 4.757848e-02 sample17 0.1178805097 2.040526e-02 sample18 0.0767858320 1.756604e-02 sample19 0.0157112113 -2.172867e-02 sample20 0.0485318300 -2.327033e-02 sample21 0.0185928176 4.777095e-02 sample22 -0.0191358702 -2.329775e-02 sample23 -0.0672994194 -1.535656e-03 sample24 0.1047476642 5.935707e-02 sample25 0.0329844953 -1.358036e-02 sample26 0.1154952052 1.741529e-02 sample27 0.0133849853 -3.590922e-02 sample28 0.0821554039 2.042376e-02 sample29 -0.0567643690 -2.123848e-02 sample30 0.1016073931 -1.134728e-03 sample31 -0.0880396372 3.670548e-02 sample32 0.0300363338 1.182406e-02 sample33 -0.0467252272 3.739254e-03 sample34 0.0783666394 1.203777e-02 sample35 0.0424227097 -1.118559e-02 sample36 -0.1107646166 -1.143464e-02 sample37 -0.0191667664 -2.246060e-02 sample38 0.0155968095 -2.909621e-02 sample39 0.0746847148 7.148218e-03 sample40 -0.0517028178 -2.137267e-02 sample41 0.0234979494 -3.723018e-02 sample42 0.0388797356 -8.557228e-03 sample43 0.0149555568 7.210002e-03 sample44 -0.1150305613 3.461805e-02 sample45 0.0846146236 3.486020e-02 sample46 0.0884426404 -3.246853e-02 sample47 0.0748644971 -8.083045e-03 sample48 -0.0012033198 -9.403647e-03 sample49 -0.0872662737 3.616245e-02 sample50 0.0066941314 -5.284863e-02 sample51 -0.0411777630 3.791830e-02 sample52 -0.0379355780 2.180834e-02 sample53 -0.0851639886 4.751761e-02 sample54 0.0288006248 7.184424e-03 sample55 -0.0164920835 5.919925e-05 sample56 0.0355115616 1.951043e-02 sample57 -0.0141146068 3.492409e-02 sample58 -0.0015636132 9.862883e-03 sample59 -0.0390656483 3.590929e-03 sample60 -0.0139454780 3.963030e-03 sample61 -0.0106410274 1.919705e-02 sample62 0.0236748439 -5.922677e-03 sample63 -0.0846790877 3.839102e-02 sample64 -0.1202581015 2.846469e-02 sample65 0.0050548584 6.328644e-03 sample66 0.0028013072 1.291807e-02 sample67 -0.1231623009 -2.112565e-02 sample68 -0.0437782161 -2.845072e-02 sample69 -0.0501199692 2.053469e-03 sample70 -0.0140278645 -2.027157e-02 sample71 0.0057489505 -4.085977e-02 sample72 0.0511212704 -5.522408e-03 sample73 0.0828141409 7.431582e-03 sample74 -0.0085959456 1.772951e-02 sample75 -0.0312180394 -6.636869e-03 sample76 -0.0519051781 -1.640191e-02 sample77 -0.0925924762 -6.907800e-03 sample78 0.1163971046 2.251122e-02 sample79 -0.0240906926 -4.887766e-02 sample80 -0.0221327065 -6.730703e-03 sample81 -0.0072114968 -2.254399e-02 sample82 0.1204416674 -9.907422e-03 sample83 0.0386739485 -1.171663e-03 sample84 0.0195988488 -1.033806e-02 sample85 -0.0877680171 -1.725057e-03 sample86 0.1023541048 -1.062501e-02 sample87 0.0425213089 -1.356865e-02 sample88 0.0244788514 1.180820e-02 sample89 0.0804276691 2.188588e-02 sample90 -0.0074639871 -6.140721e-03 sample91 -0.1278832404 -6.485140e-02 sample92 -0.0162199697 -5.048358e-02 sample93 0.0769344893 -3.045135e-03 sample94 -0.0104345587 -3.593172e-03 sample95 -0.0260058453 -2.330475e-02 sample96 -0.0025018700 -1.433516e-02 sample97 -0.0492358305 7.774183e-03 sample98 0.0279220220 -3.862141e-03 sample99 0.0813921923 3.487339e-02 sample100 -0.0428797405 7.112807e-03 sample101 0.0032855240 -4.940743e-02 sample102 0.0038439317 2.938008e-03 sample103 0.0358511139 -2.831881e-02 sample104 -0.0162784000 -1.815061e-02 sample105 0.0314853405 -4.656633e-03 sample106 0.0726456731 1.192390e-02 sample107 0.0807342975 7.508627e-03 sample108 0.0688338003 -3.336161e-03 sample109 -0.0694151950 -2.800146e-02 sample110 0.0961218924 -2.111997e-03 sample111 -0.0217900036 -2.864702e-02 sample112 0.0599954082 8.820317e-03 sample113 -0.0195006577 1.128215e-02 sample114 -0.0032126533 -2.682851e-02 sample115 -0.0251101087 -2.221077e-02 sample116 -0.0625141551 -2.137258e-02 sample117 -0.0440473375 -2.806256e-02 sample118 -0.0532042630 -7.590494e-03 sample119 -0.0848603028 -7.133574e-02 sample120 -0.0588832131 6.937326e-03 sample121 0.0613899126 2.915307e-02 sample122 0.0218424338 2.241775e-02 sample123 0.0809008460 -1.051759e-02 sample124 -0.0472109313 1.239887e-02 sample125 0.0583180947 2.521167e-03 sample126 -0.0753941872 2.256455e-02 sample127 -0.0649774209 -3.496964e-03 sample128 0.1000212216 2.908091e-02 sample129 0.0568033049 -1.269016e-02 sample130 -0.0002370832 -9.419675e-03 sample131 -0.0727030877 4.091672e-03 sample132 -0.0566219024 2.179861e-02 sample133 0.0384172955 -1.372840e-02 sample134 -0.1280862736 2.077912e-02 sample135 0.0592633273 6.106685e-04 sample136 -0.0187635410 -1.521173e-02 sample137 -0.0449958970 -9.152840e-03 sample138 -0.0211348699 -1.875415e-02 sample139 -0.0482882861 6.729304e-03 sample140 0.0468926306 -1.285498e-02 sample141 -0.0186248693 1.605439e-02 sample142 0.0328031246 9.887746e-03 sample143 0.0052919839 -1.445666e-02 sample144 -0.0140067923 -4.867248e-03 sample145 -0.0361804310 3.625323e-02 sample146 -0.0345286735 -2.493652e-02 sample147 0.0765025670 -7.714769e-03 sample148 -0.0276016641 2.420589e-02 sample149 0.0027545308 -4.653007e-02 sample150 -0.0792296010 1.831289e-02 sample151 -0.0245894512 -8.991738e-03 sample152 0.0409796547 -1.907063e-02 sample153 0.0734301757 5.528780e-02 sample154 0.0557740684 -2.487723e-02 sample155 0.0689436560 -6.127635e-03 sample156 0.0212272938 -9.747423e-03 sample157 0.0911931194 6.355708e-03 sample158 0.0220840645 -3.016357e-03 sample159 -0.0513244242 1.304175e-02 sample160 0.0246213576 1.248444e-02 sample161 -0.0100369130 -1.805391e-02 sample162 0.1078802043 4.337260e-02 sample163 -0.1017965082 5.047171e-02 sample164 0.0119430799 9.593002e-04 sample165 -0.0063708014 -5.032148e-03 sample166 -0.0283181180 1.899222e-02 sample167 -0.0872832229 1.516582e-04 sample168 0.0540714512 1.397701e-02 sample169 0.0328432652 7.104347e-03 > o2plsRes@scores$dist[[1]] ## Distinctive scores for Block 1 [,1] [,2] sample1 0.0133684846 2.195848e-02 sample2 0.0254157197 -1.058416e-02 sample3 -0.0049551479 -4.840017e-03 sample4 0.0310390570 -1.063929e-02 sample5 0.0046941318 -6.488426e-03 sample6 -0.0107406753 -1.026702e-02 sample7 -0.0225157631 2.624712e-04 sample8 0.0141320952 -9.505821e-03 sample9 0.0029681280 2.078210e-02 sample10 0.0131729174 -2.275042e-03 sample11 -0.0004164298 1.994019e-02 sample12 -0.0095211620 3.759883e-02 sample13 0.0091018604 -7.953956e-03 sample14 -0.0106557524 -9.181659e-03 sample15 -0.0249924121 3.262724e-02 sample16 -0.0156216400 1.375700e-02 sample17 -0.0019382446 1.073994e-03 sample18 -0.0221072481 -8.703592e-03 sample19 0.0146917619 -1.311712e-02 sample20 -0.0160353760 1.826290e-02 sample21 0.0035947899 -9.616341e-03 sample22 -0.0225060762 -2.532589e-03 sample23 0.0310000683 3.033060e-03 sample24 0.0499544372 1.809450e-02 sample25 0.0284442301 -1.932558e-02 sample26 0.0188220043 2.146985e-02 sample27 -0.0257763219 -1.999228e-03 sample28 0.0120888648 1.125834e-02 sample29 -0.0236482520 4.426726e-02 sample30 -0.0385486305 -2.055935e-02 sample31 -0.0181539336 -5.877838e-03 sample32 -0.0302630460 -2.607192e-03 sample33 -0.0319565715 -1.562628e-02 sample34 -0.0197970124 9.906813e-03 sample35 -0.0247412713 -5.434440e-03 sample36 -0.0386259060 -3.190394e-02 sample37 -0.0566199273 -4.192574e-02 sample38 -0.0142060273 2.259644e-02 sample39 0.0053589035 1.076485e-02 sample40 -0.0552546493 -3.819896e-02 sample41 -0.0013089975 9.278818e-05 sample42 0.0137252142 -1.664652e-02 sample43 -0.0151259626 -6.290953e-03 sample44 0.0617391754 -1.442883e-02 sample45 0.0231410886 1.163143e-03 sample46 -0.0148898209 -1.384176e-04 sample47 -0.0187252536 1.221690e-02 sample48 0.0432839432 1.416671e-02 sample49 0.0160818605 -3.588745e-02 sample50 0.0059333545 4.067003e-02 sample51 -0.0142914866 7.776270e-03 sample52 -0.0086339952 7.208917e-03 sample53 -0.0207386980 6.272432e-03 sample54 -0.0039856719 -1.316934e-02 sample55 -0.0056217017 5.692315e-03 sample56 0.0000123292 8.978290e-04 sample57 -0.0095805555 1.324253e-02 sample58 -0.0124160295 -7.326376e-03 sample59 -0.0400195442 -1.349736e-02 sample60 -0.0460063358 2.770091e-02 sample61 -0.0245266456 1.470710e-02 sample62 -0.0366022783 -3.437352e-03 sample63 0.0013742171 3.288796e-02 sample64 -0.0070599859 2.739588e-02 sample65 0.0041201911 1.498268e-02 sample66 0.0143173351 -1.968812e-02 sample67 -0.0467477531 -1.929938e-02 sample68 -0.0306751978 -1.436184e-02 sample69 -0.0125317217 4.130407e-03 sample70 -0.0068071487 8.080857e-03 sample71 0.0169170264 -7.027348e-03 sample72 -0.0346909749 -1.333770e-02 sample73 -0.0280506153 1.493843e-02 sample74 -0.0182611498 3.294697e-03 sample75 -0.0120563964 8.974612e-03 sample76 0.0001437236 -4.253184e-02 sample77 0.0065330299 -5.252886e-02 sample78 0.0288278141 -1.127782e-02 sample79 0.0503961481 -1.023318e-02 sample80 -0.0207693429 3.648391e-02 sample81 0.0163562768 -9.074596e-03 sample82 -0.0084317129 -1.478976e-02 sample83 -0.0474097918 -1.103126e-02 sample84 0.0177181395 -7.191197e-03 sample85 -0.0342718548 -3.082360e-02 sample86 -0.0261671791 -1.089491e-02 sample87 -0.0009486358 -2.411514e-02 sample88 0.0020528931 -2.894615e-02 sample89 -0.0189361111 -2.638639e-03 sample90 -0.0009863658 -2.390075e-02 sample91 -0.0124352695 8.153234e-02 sample92 0.0564264106 -8.909537e-03 sample93 -0.0081461774 1.570851e-02 sample94 -0.0054896581 1.547251e-02 sample95 0.0224073150 -4.374348e-04 sample96 0.0173528924 -3.050441e-03 sample97 0.0067948115 5.008237e-03 sample98 -0.0116030825 1.498764e-02 sample99 0.0246422688 -4.054795e-03 sample100 -0.0069420745 -4.846343e-04 sample101 0.0124923691 3.091503e-02 sample102 0.0650835386 -1.367400e-02 sample103 -0.0042741828 7.855985e-03 sample104 0.0250591040 -4.171938e-03 sample105 0.0157516368 -3.121990e-02 sample106 0.0060593853 -5.101693e-03 sample107 -0.0098329626 1.044506e-02 sample108 0.0044269853 4.142036e-03 sample109 0.0572473486 1.517542e-02 sample110 0.0090474827 -5.119868e-03 sample111 0.0444263015 7.983232e-03 sample112 -0.0131765484 -9.696342e-04 sample113 0.0241047399 6.706740e-03 sample114 0.0074558775 -4.728652e-03 sample115 0.0611851433 1.117210e-02 sample116 0.0432646951 -1.380556e-02 sample117 0.0516750066 -3.575617e-02 sample118 0.0139942100 -3.279138e-03 sample119 0.0291722987 5.587946e-02 sample120 0.0103515853 -1.690016e-03 sample121 -0.0091396331 3.552116e-02 sample122 0.0260431679 -7.583975e-03 sample123 -0.0076666389 -1.628489e-02 sample124 0.0283466326 3.127845e-03 sample125 0.0016472378 -2.770692e-02 sample126 -0.0286529417 3.489336e-02 sample127 -0.0010224500 7.483214e-03 sample128 0.0209049296 2.572016e-02 sample129 -0.0218184878 -1.755347e-02 sample130 -0.0005009620 -1.697978e-02 sample131 -0.0134032968 4.637390e-03 sample132 0.0198526786 5.723983e-04 sample133 0.0088812957 -9.988115e-03 sample134 -0.0137484514 1.172591e-02 sample135 -0.0220314568 1.347465e-02 sample136 -0.0185173353 5.168079e-03 sample137 -0.0248352123 -9.472788e-03 sample138 0.0301635767 -1.175283e-02 sample139 -0.0173576929 -3.872592e-02 sample140 -0.0262157762 2.456863e-02 sample141 0.0058369763 -1.420854e-02 sample142 0.0207886071 -1.188764e-02 sample143 0.0092832598 -1.324238e-02 sample144 0.0028442140 3.627979e-03 sample145 0.0199749569 2.862202e-03 sample146 -0.0182236697 1.726556e-03 sample147 -0.0282519995 -2.825595e-02 sample148 0.0065435868 -1.572917e-02 sample149 0.0158233820 -2.159451e-02 sample150 -0.0177383738 -3.020633e-03 sample151 0.0245166984 -6.888241e-03 sample152 0.0107259913 3.314630e-02 sample153 0.0550963965 3.758760e-02 sample154 -0.0131452472 -8.153903e-04 sample155 -0.0211742574 2.642246e-03 sample156 -0.0117803505 2.698265e-02 sample157 -0.0096167165 1.433840e-02 sample158 -0.0101754772 9.137620e-03 sample159 0.0120662931 -2.565236e-02 sample160 -0.0132238202 2.916023e-03 sample161 0.0274491966 -1.748284e-02 sample162 0.0012482909 3.152261e-02 sample163 0.0042031315 1.830701e-02 sample164 0.0174896157 -1.175915e-02 sample165 0.0097517662 -6.119019e-03 sample166 0.0190134679 -1.121582e-02 sample167 -0.0044140836 4.665585e-03 sample168 0.0049689168 -1.941822e-02 sample169 -0.0209802098 3.498729e-03 > o2plsRes@scores$dist[[2]] ## Distinctive scores for Block 2 [,1] [,2] sample1 -0.0515543627 -0.0305856787 sample2 -0.0144993256 0.0236342950 sample3 -0.0371833108 -0.0140263348 sample4 0.0068945388 -0.0132539692 sample5 0.0215035333 -0.0663338101 sample6 -0.0187055152 0.0088773016 sample7 -0.0061521552 0.0064029054 sample8 -0.0210874459 0.0334652901 sample9 0.0516865043 -0.0291142799 sample10 0.0059440366 -0.0527217447 sample11 0.0393010793 -0.0200624712 sample12 -0.0420837100 0.0131331362 sample13 0.0333252565 0.0818552509 sample14 -0.0190062644 0.0160202175 sample15 -0.0030968049 -0.0189230681 sample16 -0.0004452158 0.0018880102 sample17 -0.0185848615 0.0240170131 sample18 -0.0273093598 0.0230213640 sample19 -0.0217761111 -0.0445894441 sample20 0.0245820821 0.0159812738 sample21 0.0034527644 -0.0400016054 sample22 -0.0340789054 0.0039289109 sample23 -0.0010344929 -0.0310161212 sample24 0.0289468503 0.0760962436 sample25 -0.0119098496 -0.0122798760 sample26 -0.0181001057 0.0517892852 sample27 0.0050465417 -0.0086515844 sample28 0.0057491502 0.0358830107 sample29 -0.0051104246 0.0116605117 sample30 -0.0103085904 0.0039678538 sample31 -0.0319929858 0.0090606113 sample32 -0.0036232521 -0.0328202010 sample33 -0.0534742153 0.0024751837 sample34 -0.0067495749 -0.0111000311 sample35 0.0378745721 0.0465929296 sample36 0.0647886800 0.0359987924 sample37 0.0488441236 0.0492906912 sample38 -0.0251514062 0.0197110110 sample39 -0.0085428066 -0.0105117852 sample40 0.0379324087 0.0440810741 sample41 -0.0044199152 -0.0128820644 sample42 -0.0292553573 -0.0067045265 sample43 -0.0077829155 -0.0510178219 sample44 0.0045122248 0.0479660309 sample45 -0.0074444298 -0.0051116726 sample46 -0.0088025512 0.0196186661 sample47 0.0076696301 0.0215947965 sample48 0.0290108585 -0.0175568376 sample49 -0.0141754858 0.0184717099 sample50 0.0006282201 -0.0233054373 sample51 0.0441995177 -0.0410022921 sample52 0.0715329391 -0.0399499475 sample53 -0.0095954087 -0.0029140909 sample54 0.0048933768 -0.0281884386 sample55 0.0327325487 -0.0532290012 sample56 0.0323068984 -0.0256595538 sample57 0.0806603122 -0.0286748097 sample58 -0.0064792049 -0.0006945349 sample59 0.0088958941 0.0067389649 sample60 0.0874124612 0.0431964341 sample61 0.0577604571 -0.0326112099 sample62 -0.0313318464 0.0224391756 sample63 -0.0233625220 0.0125110562 sample64 -0.0086426068 0.0148770341 sample65 0.0025256193 -0.0404466327 sample66 0.0006014071 -0.0471576264 sample67 0.0706087042 0.0516228406 sample68 0.0082301011 0.0033109509 sample69 -0.0475076743 0.0001452708 sample70 -0.0600773716 0.0089986962 sample71 -0.0096321627 -0.0050761187 sample72 -0.0031773546 -0.0166221542 sample73 -0.0113700517 -0.0191726684 sample74 -0.0014179662 -0.0608101325 sample75 0.0041911740 -0.0399981269 sample76 -0.0055326449 0.0353114263 sample77 -0.0260214459 0.0305731380 sample78 -0.0119267436 0.0632236007 sample79 0.0186017239 0.0027402910 sample80 0.0241047889 -0.0472697181 sample81 -0.0220288317 -0.0079577210 sample82 -0.0180751258 0.0639051029 sample83 -0.0256671713 -0.0125898269 sample84 0.0161392598 -0.0567222449 sample85 0.0139988188 0.0322763454 sample86 -0.0198382995 0.0389225776 sample87 0.0266270281 -0.0032979996 sample88 0.0515677078 0.0117902495 sample89 0.0014022125 -0.0140510488 sample90 -0.0375949749 0.0044004551 sample91 0.0310397965 0.0440610926 sample92 0.0270570567 0.0324380452 sample93 -0.0215009202 0.0063993941 sample94 -0.0415702912 -0.0037692077 sample95 -0.0168416047 0.0010019120 sample96 -0.0285582661 -0.0187991000 sample97 -0.0490843868 -0.0266760748 sample98 -0.0171579033 -0.0112897471 sample99 -0.0271316525 0.0232395583 sample100 -0.0301789816 0.0305498693 sample101 -0.0264371151 0.0170723968 sample102 0.0012767734 -0.0248949597 sample103 0.0055214687 -0.0030040587 sample104 0.0251346074 -0.0165212671 sample105 0.0062424215 -0.0400309901 sample106 0.0069768684 0.0154982315 sample107 -0.0315912602 -0.0118883820 sample108 -0.0109690679 0.0023637162 sample109 -0.0014762845 0.0165583675 sample110 0.0036971063 0.0168260726 sample111 -0.0071624739 -0.0345651461 sample112 0.0046098120 -0.0048009350 sample113 0.0082236008 -0.0383233357 sample114 -0.0293642209 -0.0165595240 sample115 -0.0003260453 0.0135805368 sample116 0.0183575759 0.0665377581 sample117 0.0227640036 -0.0012287760 sample118 0.0015695248 0.0472617382 sample119 0.0190084932 0.0590034062 sample120 -0.0449645755 0.0072755697 sample121 0.0077307184 0.0104738937 sample122 -0.0027132063 -0.0394983138 sample123 0.0016959300 0.0028593594 sample124 -0.0365091615 0.0040382925 sample125 -0.0053658663 -0.0316029164 sample126 -0.0458032408 0.0019165544 sample127 -0.0494064872 0.0088209044 sample128 -0.0155454766 0.0186819802 sample129 -0.0184340400 0.0038684312 sample130 -0.0303640987 -0.0052225766 sample131 -0.0088697422 0.0156339713 sample132 -0.0433916471 -0.0154075483 sample133 0.0204029276 -0.0282209049 sample134 0.0175513332 0.0262883962 sample135 0.0029009925 0.0017003151 sample136 -0.0367997573 -0.0072249751 sample137 -0.0348600323 0.0075400273 sample138 -0.0044063824 -0.0053752428 sample139 0.0073103935 0.0308956174 sample140 0.0039925654 -0.0167019605 sample141 -0.0184093462 -0.0387953445 sample142 0.0268670676 -0.0239229634 sample143 0.0421049126 -0.0110888235 sample144 0.0017253664 -0.0341766012 sample145 0.0681741320 -0.0073526377 sample146 -0.0239965222 0.0118396767 sample147 -0.0063453522 0.0183130585 sample148 0.0230825251 -0.0379753037 sample149 0.0223298673 0.0188909118 sample150 0.0055709108 0.0174179009 sample151 0.0039177786 -0.0233533275 sample152 0.0134325667 0.0302344591 sample153 0.0511990309 0.0730230140 sample154 0.0006698324 0.0154177486 sample155 0.0032926626 -0.0288651601 sample156 -0.0016463495 -0.0474657733 sample157 -0.0045857599 0.0154934573 sample158 0.0201775524 -0.0332982124 sample159 -0.0086909001 0.0073496711 sample160 0.0295437331 -0.0555734536 sample161 0.0332754288 0.0033779619 sample162 0.0121954537 0.0433540412 sample163 -0.0173490933 0.0227219128 sample164 0.0143374783 -0.0453542590 sample165 0.0343612593 -0.0511194536 sample166 -0.0157536004 0.0094621170 sample167 -0.0179654624 -0.0006982358 sample168 -0.0033829919 0.0060747155 sample169 0.0116231468 -0.0015112800 > > ## 3.3 Plotting VAF > > # DISCO-SCA plotVAF > plotVAF(discoRes) > > # JIVE plotVAF > plotVAF(jiveRes) > > > ######################### > ## PART 4. Plot Results > > # Scores for common part. DISCO-SCA > plotRes(object=discoRes,comps=c(1,2),what="scores",type="common", + combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL, + background=TRUE,palette=NULL,pointSize=4,labelSize=NULL, + axisSize=NULL,titleSize=NULL) Warning message: `aes_string()` was deprecated in ggplot2 3.0.0. ℹ Please use tidy evaluation idioms with `aes()`. ℹ See also `vignette("ggplot2-in-packages")` for more information. ℹ The deprecated feature was likely used in the STATegRa package. Please report the issue to the authors. > > # Scores for common part. JIVE > plotRes(object=jiveRes,comps=c(1,2),what="scores",type="common", + combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL, + background=TRUE,palette=NULL,pointSize=4,labelSize=NULL, + axisSize=NULL,titleSize=NULL) > > # Scores for common part. O2PLS. > p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common", + combined=FALSE,block="expr",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common", + combined=FALSE,block="mirna",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > legend <- g_legend(p1) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + legend,heights=c(6/7,1/7)) > > # Combined plot of scores for common part. O2PLS. > plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="common", + combined=TRUE,block=NULL,color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > > > # Scores for distinctive part. DISCO-SCA. (two plots one for each block) > p1 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual", + combined=FALSE,block="expr",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual", + combined=FALSE,block="mirna",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > legend <- g_legend(p1) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + legend,heights=c(6/7,1/7)) > > # Combined plot of scores for distinctive part. DISCO-SCA > plotRes(object=discoRes,comps=c(1,1),what="scores",type="individual", + combined=TRUE,block=NULL,color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > > # Combined plot of scores for common and distinctive part. O2PLS (two plots one for each block) > p1 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both", + combined=FALSE,block="expr",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both", + combined=FALSE,block="mirna",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > legend <- g_legend(p1) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + legend,heights=c(6/7,1/7)) > > # Combined plot of scores for common and distinctive part. DISCO (two plots one for each block) > p1 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both", + combined=FALSE,block="expr",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both", + combined=FALSE,block="mirna",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > legend <- g_legend(p1) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + legend,heights=c(6/7,1/7)) > > # Loadings for common part. DISCO-SCA. (two plots one for each block) > p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common", + combined=FALSE,block="expr",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common", + combined=FALSE,block="mirna",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + heights=c(6/7,1/7)) > > > # Loadings for distinctive part. DISCO-SCA. (two plots one for each block) > p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual", + combined=FALSE,block="expr",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual", + combined=FALSE,block="mirna",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + heights=c(6/7,1/7)) > > > # Combined plot for loadings from common and distinctive part (two plots one for each block) > p1 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both", + combined=FALSE,block="expr",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both", + combined=FALSE,block="mirna",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + heights=c(6/7,1/7)) > > > > ## Plot scores and loadings togheter: Common components DISCO-SCA > p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common", + combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL, + background=TRUE,palette=NULL,pointSize=4,labelSize=NULL, + axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common", + combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL, + background=TRUE,palette=NULL,pointSize=4,labelSize=NULL, + axisSize=NULL,titleSize=NULL) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + heights=c(6/7,1/7)) > > > ## Plot scores and loadings togheter: Common components O2PLS > p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common", + combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL, + background=TRUE,palette=NULL,pointSize=4,labelSize=NULL, + axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common", + combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL, + background=TRUE,palette=NULL,pointSize=4,labelSize=NULL, + axisSize=NULL,titleSize=NULL) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + heights=c(6/7,1/7)) > > > ## Plot scores and loadings togheter: Distintive components DISCO-SCA > p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual", + combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL, + background=TRUE,palette=NULL,pointSize=4,labelSize=NULL, + axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual", + combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL, + background=TRUE,palette=NULL,pointSize=4,labelSize=NULL, + axisSize=NULL,titleSize=NULL) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + heights=c(6/7,1/7)) > > > > > proc.time() user system elapsed 17.538 0.652 18.331
STATegRa.Rcheck/STATegRa-Ex.timings
name | user | system | elapsed | |
STATegRaUsersGuide | 0.001 | 0.001 | 0.002 | |
STATegRa_data | 0.153 | 0.012 | 0.166 | |
STATegRa_data_TCGA_BRCA | 0.001 | 0.001 | 0.003 | |
bioDist | 0.407 | 0.024 | 0.432 | |
bioDistFeature | 0.303 | 0.020 | 0.326 | |
bioDistFeaturePlot | 0.334 | 0.024 | 0.361 | |
bioDistW | 0.300 | 0.021 | 0.323 | |
bioDistWPlot | 0.284 | 0.017 | 0.302 | |
bioMap | 0.003 | 0.001 | 0.003 | |
combiningMappings | 0.010 | 0.001 | 0.012 | |
createOmicsExpressionSet | 0.096 | 0.002 | 0.099 | |
getInitialData | 0.630 | 0.168 | 0.803 | |
getLoadings | 0.649 | 0.219 | 0.871 | |
getMethodInfo | 0.733 | 0.126 | 0.864 | |
getPreprocessing | 0.809 | 0.661 | 1.483 | |
getScores | 0.756 | 0.165 | 0.930 | |
getVAF | 0.590 | 0.121 | 0.718 | |
holistOmics | 0.003 | 0.002 | 0.004 | |
modelSelection | 1.861 | 1.320 | 3.202 | |
omicsCompAnalysis | 3.692 | 0.244 | 3.972 | |
omicsNPC | 0.002 | 0.002 | 0.003 | |
plotRes | 5.578 | 0.306 | 5.923 | |
plotVAF | 4.805 | 0.211 | 5.050 | |